A Bias-Free Training Paradigm for More General AI-generated Image Detection
This addresses the issue of unreliable forensic detection for AI-generated images, which is crucial for security and media integrity, though it is incremental as it focuses on dataset design rather than algorithmic breakthroughs.
The paper tackles the problem of AI-generated image detectors failing to generalize to real-world applications due to training data biases, and proposes a bias-free training paradigm that improves generalization and robustness across 27 generative models.
Successful forensic detectors can produce excellent results in supervised learning benchmarks but struggle to transfer to real-world applications. We believe this limitation is largely due to inadequate training data quality. While most research focuses on developing new algorithms, less attention is given to training data selection, despite evidence that performance can be strongly impacted by spurious correlations such as content, format, or resolution. A well-designed forensic detector should detect generator specific artifacts rather than reflect data biases. To this end, we propose B-Free, a bias-free training paradigm, where fake images are generated from real ones using the conditioning procedure of stable diffusion models. This ensures semantic alignment between real and fake images, allowing any differences to stem solely from the subtle artifacts introduced by AI generation. Through content-based augmentation, we show significant improvements in both generalization and robustness over state-of-the-art detectors and more calibrated results across 27 different generative models, including recent releases, like FLUX and Stable Diffusion 3.5. Our findings emphasize the importance of a careful dataset design, highlighting the need for further research on this topic. Code and data are publicly available at https://grip-unina.github.io/B-Free/.